A Perturbation Framework for Convex Minimization and Monotone Inclusion Problems with Nonlinear Compositions

We introduce a framework based on Rockafellar's perturbation theory to analyze and solve general nonsmooth convex minimization and monotone inclusion problems involving nonlinearly composed functions as well as linear compositions. Such problems have been investigated only from a primal perspective and only for nonlinear compositions of smooth functions in finite-dimensional spaces in the absence of linear compositions. In the context of Banach spaces, the proposed perturbation analysis leads to the construction of a dual problem and of a maximally monotone Kuhn--Tucker operator which is decomposable as the sum of simpler monotone operators. In the Hilbertian setting, this decomposition leads to block-iterative primal-dual proximal algorithms that fully split all the components of the problem and capture state-of-the-art existing methods as special cases